The imaging equipment working in the atmosphere will not only be limited by the performance of the imaging system, but also be affected by turbulence. In the fields of astronomical observation, ground-based remote sensing and remote monitoring, there is an urgent need for corresponding methods and technologies to eliminate the impact of atmospheric turbulence and obtain clear images. With the development of computer technology, atmospheric optics theory and image processing technology, more and more researchers hope to combine deep learning technology with atmospheric turbulence theory to reduce the impact of turbulence on imaging and obtain clear and stable images. In this paper, a turbulence image restoration technique based on Generative Adversarial Networks (GAN) is proposed, which is divided into generator network and discriminator network. The generator network is used to convert blurred images affected by turbulence into clear images. The discriminator network is used to compare the converted image with the real clear image to determine whether the image is real or generated. After the whole GAN is optimized and trained, the image transformed by the generator and the real and clear image cannot be distinguished from each other. Because the training of the GAN requires a large number of corresponding samples, it is difficult to obtain the images affected and unaffected by turbulence at the same time in real life, so this paper uses the statistical characteristics of turbulence to simulate a large number of images affected by turbulence. We used the trained GAN model to simulate turbulence image restoration and got some achievements.
With the proposal of ocean strategic planning, the measurement of optical parameters and the study of atmospheric models have become particularly important. In order to solve the problem that traditional sun-photometer cannot track the sun accurately on the moving platform at sea, a design scheme of shipboard sun-photometer is proposed for the limitation of water vapor and aerosol measurement. In this paper, the overall structure of shipboard sun-photometer and tracking technology route are described in detail,and the depth analysis is carried out in three aspects:the sloshing compensation of the hull,how to locate the azimuth of the sun at sea,the size and accuracy of the tracking field of view. Firstly, the fisheye imaging system is combined with the astronomical and celestial trajectory tracking to perform rough tracking of the sun in the large field of view, and then fine tracking by the CCD image processing technology of the small field of view. Double compensation for hull sway through the attitude sensing system and the gyrostabilization platform. The measurement of marine water vapor and atmospheric aerosols is accomplished by fully automated real-time precise tracking of the sun. Finally, the key indicators were calculated and analyzed, and the tracking accuracy and measurement field of view were determined.
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